Sequence clustering and labeling for unsupervised query intent discovery

One popular form of semantic search observed in several modern search engines is to recognize query patterns that trigger instant answers or domain-specific search, producing semantically enriched search results. This often requires understanding the query intent in addition to the meaning of the query terms in order to access structured data sources. A major challenge in intent understanding is to construct a domain-dependent schema and to annotate search queries based on such a schema, a process that to date has required much manual annotation effort. We present an unsupervised method for clustering queries with similar intent and for producing a pattern consisting of a sequence of semantic concepts and/or lexical items for each intent. Furthermore, we leverage the discovered intent patterns to automatically annotate a large number of queries beyond those used in clustering. We evaluated our method on 10 selected domains, discovering over 1400 intent patterns and automatically ann...

• Type: Journal
• Year: 2012
• Where: WSDM
• Authors: Jackie Chi Kit Cheung, Xiao Li
• Keywords: Data Mining, Information Storage And Retrieval, Seattle Washington Usa, Unsupervised Method, WSDM 2012